非Poisson点过程的综述与分类
Summarization and Classification of Non-Poisson Point Processes
摘要：
非泊松点过程的拟合模型是复杂的，缺乏易于处理的模型的数据。通过使用独立且同分布的实现的大样本和统计学习，可以通过找到可以有效地识别每种类型的单个实现的分类规则来识别拟合的缺失。该方法需要比目前使用的更广泛的描述性统计，以及一个新的模型拟合概念，该概念来自于如何判断物理定律以拟合数据。
英文摘要：
Fitting models for non-Poisson point processes is complicated by the lack of tractable models for much of the data. By using large samples of independent and identically distributed realizations and statistical learning, it is possible to identify absence of fit through finding a classification rule that can efficiently identify single realizations of each type. The method requires a much wider range of descriptive statistics than are currently in use, and a new concept of model fitting which is derive from how physical laws are judged to fit data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
